A Systematic Mapping Study on Software Ecosystems

Abstract

Software ecosystem is an approach that investigates the complex relationships among companies in the software industry. Companies work cooperatively and competitively in order to achieve their strategic objectives. They must engage in a new perspective considering both their own business and third party ones. Inspired from properties by natural and business ecosystems, a software ecosystem covers technical and business aspects of software development as well as partnership among companies. In this paper, we undertake a systematic mapping study to present a wide review of primary studies on software ecosystems. Systematic mapping is a methodology that gives, after a systematic research process, a visual summary map of its results. The search procedure identified 1026 studies, of which 44 were identified as relevant to answer our research questions. This study mapped what is currently known about software ecosystems perspective. We conclude that software ecosystems research is concentrated in 8 main areas in which the most relevant are open source software, ecosystem modeling, and business issues. The paper is intended to practitioners and academics investigating the field of software ecosystems. It contributes to summarize the body of knowledge in the field and direct efforts for future research in software ecosystems.

Keywords

• software ecosystem
• digital ecosystem
• business ecosystem
• systematic mapping study

1 Introduction

Increasing attention is being paid to connectivity and dependency in relationships between companies. Innovations no longer originate in a single organization; rather they are co-innovations from different players [ 1 ]. Companies co-evolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations [ 2 ]. These loose networks of suppliers, distributors, outsourcing companies, developers of related products or services, technology providers, and a host of other organizations affect and are affected by, the creation and delivery of a company’s own offerings [ 3 ]. According to these viewpoints, researchers have coined a new perspective to analyze the software industry, called software ecosystems. This is an emergent field inspired on concepts from Moore and Iansiti’s [ 2,3 ] business and biological ecosystem. This paper provides an overview of the current literature in software ecosystem by conducting a systematic mapping study. Systematic mapping is a methodology that is frequently used in medical research, but that has largely been neglected in Software Engineering [ 4 ]. The usual outcome is a visual map classifying the results. It requires less effort than a systematic literature review while providing a more coarse-grained overview. The remainder of this paper is organized as follows. In section 2, we present the research method. We describe research questions, inclusion/exclusion criteria, sources of studies, search strategy, data extraction and synthesis of findings. In section 3, we present the results of this systematic mapping study. In section 4 we outline our conclusions, limitations of this work and discuss future research directions.

2 Research Method

A review protocol specifies the methods that will be used to undertake a specific systematic review and reduces the possibility of researcher bias [ 5 ]. Although systematic literature review and mapping study do not share all research procedures, similar processes for searching are explicitly defined in the research protocol and reported as part of the outcomes [ 6 ]. A review protocol is an essential element to conduct a secondary study that includes a description and rationale for the research questions and the proposed methods. It also includes details of how different types of studies will be located, appraised, and synthesized [ 7,8 ]. Thus, a protocol was developed in order to define the main guidelines for conducting this study. According to Brereton and others [ 8 ] a systematic map is used to describe the kinds of research activity that have been undertaken and describes the studies rather than extracting specific details. That is, it does provide a context for the later synthesis. According to Kitchenham [ 4 ] a systematic map is a method that can be conducted to get an overview of a particular research area. After this, the state of evidence in specific topics can be investigated using a systematic review, if necessary. A research question is a precisely stated question that guides the review [ 9 ]. According to Budgen and others [ 6 ], in systematic mapping study, the research question itself is likely to be much broader than in systematic review. This is necessary in order to adequately address the wider scope of study. Following these guidelines, we specified four research questions (RQs) in order to characterize the field of software ecosystems: (RQ1) What are the main characteristics of a Software Ecosystem? (RQ2) What is currently known about the benefits, challenges and limitations of Software Ecosystems? (RQ3) What are the implications of software ecosystem studies for research and practice in Software Engineering? (RQ4) What are the main areas studied from the perspective of Software Ecosystems? An important step in the search for studies is to decide on criteria for including and excluding papers [ 9 ]. Inclusion and exclusion criteria are used to exclude studies that are not relevant to answer the research questions. As pointed out by Petticrew and Roberts [ 7 ], inclusion and exclusion criteria step is one of the activities of a mapping study very similar to a systematic literature review. It comprises selecting the appropriate primary studies from literature. The following list shows the inclusion criteria adopted: (1) Only studies written in English; (2) Studies dealing and referencing any of the subjects related to software ecosystems in their title or abstract; (3) Master and Doctorate theses; (4) Studies unrestricted publication date; (5) “grey literature” [ 7 ] including reports published independently by academic and industrial consulting organizations. The exclusion criteria were: (1) Repeated studies found in different search engines. In this case, just one study was considered; (2) Duplicate studies reporting similar results. In this case, only the most complete study was considered; (3) Inaccessible papers and books.

Now, the search strategy and data sources are presented. According to Kitchenham [ 10 ], researchers should specify their rationale for the use of electronic or manual search or a combination of both. In this study, we used both electronic and manual search procedures. In the manual search, we decided to find papers of a specific set of conferences, researchers’ websites, and asking experts in the field of software ecosystems. The justification for not using just electronic search procedures was supported by recommendations from [ 10,11 ] who advocate the use of manual search in order to obtain a broader list of potential papers to review. According to Petticrew and Roberts [ 7 ] electronic databases are not the only source of literature, and sometimes they are not even the most useful. Another reason was due to the fact that software ecosystems is a novel field; therefore a manual search brings extra confidence that more relevant papers might be found. The electronic search was applied on the following Search Engines: IEEEXplore Digital Library, ACM Digital Library, Elsevier ScienceDirect, EI Compendex, Scopus and Web of Science. These digital libraries were selected because they are the most important repositories for research in computer science. For the manual search, we considered 11 repositories, where 9 were experts’ personal websites and 2 were the following conferences: International Workshop on Software Ecosystems (IWSECO) and ACM Conference on Management of Emergent Digital Ecosystems (MEDES). We used an approach to derive terms from the research questions to create the search string. The strategy was: (1) We derived the main search terms; (2) checked the keywords for relevant papers already known and (3) looked for alternative forms of the terms such as synonyms and relevant keywords. After that, we used Boolean operators OR and AND to incorporate them into the search string. The first segment consisted of synonyms of software ecosystem and the second one was derived from the main research questions terms. Table 1 shows the final search string.

3 Results

In order to identify relevant studies, we used the following steps. First, we applied the search string in the 6 search engines listed above. The digital databases provided 668 papers, but just 418 of them were available for download. After downloading, only 117 papers were included according the inclusion and exclusion criteria. Since we used several databases, many papers were duplicated. Finally, our list was reduced to 56 papers. In the manual research, we found initially 358 papers. But just 271 were available for download and only 127 papers were included. The sum of the both manual and digital search was 183 papers. However, by crosschecking both manual and digital databases we found some duplications and the final number decreased from 183 to 141. After that, we reached the last stage when all papers were read and 44 relevant papers were selected for this systematic mapping study. Fig. 1 summarizes the selection process and presents the number of papers identified at each step.

Spreadsheets were created to record included and excluded papers for each stage. For reading and extracting data, we used the Mendeley (http://www.mendeley.com/). We created a form to record details about how each paper would answer the four research questions. In the synthesis of findings, we adopted the method similar to the one used by Dyba and others [ 12 ]. We used a process based on the method of constant comparison used in qualitative analysis. The process begins with coding the field notes to extract pieces of text relevant to a particular theme or idea that is of interest in the study. Finally, the results are examined for underlying themes and explanations of phenomena [13]. In the Appendix, we present the complete list of selected studies enumerated from S1 to S44. Fig. 2 illustrates the year wise distribution of selected papers. It is possible to notice an increase in the number of papers in the last two years. This suggests a growing interest by the community in the field. Moreover, it indicates this topic is relatively recent in publications. The oldest study found was published in 2003 (S43). We emphasize that papers like [ 2, 3 ] are not in the selected studies due to the fact that they do not focus specifically on software ecosystem. Instead, these papers primarily address the business ecosystem topic. Following, we discuss how each research question was answered. (RQ-1) What are the main characteristics of a Software Ecosystem? Table 2 lists the main characteristics of software ecosystems identified in the reviewed studies. A notable result is the relevance of open source models in the context of software ecosystems. In fact, literature in open source software reinforces the importance of collaboration among players to build a mature open source platform. Other results state that software ecosystems are linked to natural ecosystems, software product lines, and business ecosystems. These fields are considered by many authors the origins of software ecosystem research. Table 3 presents the most common terms and acronyms related to software ecosystems. Given that software ecosystem is an emergent field, different research and industry communities have been investigating the area independently. Therefore, there is a lack of widely agreed terminology and characteristics of what constitutes a software ecosystem.

Mobile Learning Ecosystems / Mobile Ecosystem Free Open Source Software Ecosystem, Ecosystem Digital (Business) Ecosystem

Open

(RQ-2) What is currently known about the benefits, challenges and limitations of Software Ecosystems?

Benefits

Fosters the success of software, the co-evolution and innovation inside the organization, increase attractiveness for new players Decreases costs involved in software development and distribution Helps analyzing and understanding the software architecture in order to decide which platform to use Supports the cooperation and knowledge sharing among multiple and independent entities.

Enables analysis of requirements communication among stakeholders Comes as alternative to overcome the challenges during design and maintenance of distributed applications Provides help to the tasks of business identification, product architecture design, risk identification Provides information for the product line manager regarding software dependencies Study ID 1,2,4,7 9,10,23

3,10 11,23,24 20,33

30 16,36 19 32

Challenges and Limitations Study ID

rEesptraebsleisnhtaintigonreolaftipoenosphliepasnbdettwheeiernknecoowslyesdtegmeinactthoerseacnodsypstreompomsinogdealninagd. equate 2, 15 Several key architectural challenges such as: platform interface stability, evolution management, security, reliability, how to support the business 5,8,11,21,2 strategy, suitable architectures to support open source style development; how 7 open and flexible an architectural is. Heterogeneity of software licenses and systems evolution in an ecosystem. Organizations must manage these issues in order to decrease risks of 5,31,41 dependence.

Companies have difficulty at establishing a set of resources in order to differentiate from competitors. It is necessary a correct engagement of the 3,7 keystone organization in the social dimension. cToemchmnuicnailcatioanndof resqouciiore-moregnatnsizinatgioenoaglraphbiacrrdiiesrtsribufteodr procjoeocrtds.ination and 16,30 Infrastructure and tools for fostering social interaction, decision-making and development across organizations involved in both open source and proprietary 9,14,17,28 ecosystems.

RQ-3) What are the implications of software ecosystem studies for research and practice in Software Engineering?

The findings indicate a strong significance of academic institutions involved in the software ecosystem field with 70% (31 studies) of papers. The most active academic institutions are: Utrecht University, in The Netherlands (S5, S11, S12, S15, S18, S19, S29 and S41); University College London, in the UK (S5, S18, S19 and S29); Boston University, in the USA (S20, S21, S22, S23, S24 and S26), Babson College, in the USA (S20, S21, S23) and University of Lugano, in Switzerland (S4, S37 and S38). We also mention London School of Economics in the UK (S36, S40) and Imperial College London (S39). Although there are no many papers of these last two institutions it appears that there is an emergent group of researchers studying this topic. In addition, industrial institutions and government published 9 papers (S8, S10, S12, S13, S24, S27, S28, S33 and S37) and 1 paper (S34), respectively. This result suggests that the field is also investigated from the industrial standpoint.

Regarding the research methods adopted by selected studies, we found a strong importance of theoretical studies, with 12 papers (S5, S7, S8, S10, S16, S28, S33, S34, S40, S42, S43 and S44). On the other hand, we found some studies that applied qualitative methods, in special the case study method with 10 papers (S12, S13, S14, S15, S18, S19, S21, S27, S29 and S41). This means that many researchers have been conducting foundation studies that aim to define or classify the characteristics of software ecosystems. Regarding the empirical papers, we observed that case studies were conducted with varying level of rigor. The majority of studies were primary studies; only 2 papers reported ad hoc literature reviews (S9 and S40). The topics addressed by these studies, we identified that many studies point software evolution (S31, S33, S34, S36, S37, S38, S39 and S43) and co-innovation (S3, S7, S43 and S44) like an essential property of a product that sounds like a vital implication for the industry development. We also identified that many studies have proposed approaches for software ecosystem modeling, conceptual models or ecosystem analysis (S4, S5, S12, S15, S20, S22, S40, S41, S43 and S44). (RQ-4) What are the main areas studied from the perspective of Software Ecosystems? development. 10 papers present modeling techniques to represent or analyze software ecosystems. Given that several authors come from the software engineering field, we found 8 papers focusing on software evolution as part of a software ecosystem strategy, 7 papers on software architecture and 4 papers relating software ecosystems to software product lines. This demonstrates the relevance of traditional software engineering areas to the current body of knowledge in software ecosystems. From the managerial perspective, we found 7 papers dealing with business aspects of software ecosystems and 4 papers on software co-innovation. Finally, 5 papers present results on operating systems. These studies describe ecosystems such as Microsoft, SAP and Linux. It is important to mention that some papers cover more than one aspect found in the Fig. 3. Both S43 and S44, for instance, cover areas such as operating systems, software architecture, open source, business and software evolution.

Software Coinnovation (4)

Software

Evolution (8)

Business (7)

Operating

Systems (5) 16 14 12 10 8 6 4 2 0

Software

Product Line (4)

Software

Architecture (7)

Software

Ecosystem

Modeling (10)

Open Source (15)

4 Discussion

This paper presented a mapping study on software ecosystems. A total of 44 papers were included in the study. To the best of our knowledge, this is the first study aiming to classify current research in software ecosystem. Secondary reviews like mapping studies and systematic literature reviews are important methods to summarize and provide overview of a maturing discipline [ 4 ]. The main limitations of this mapping study are eventual omission of papers and bias in the extraction data. Results indicate that our choice of using manual search was crucial to increase the number of studies, 61% of papers were found through this strategy. To aid the selection of relevant papers, we contacted experts to identify more papers. Even though this is considered a useful strategy to conduct mapping studies, this approach may cause some bias. Another potential risk that we may have missed relevant papers is due to the lack of agreed terminology for software ecosystems and to the possible existence of relevant papers that do not mention the keywords we chose. With respect to bias in the data extraction, we had some difficulties to extract relevant information from the papers. Several papers did not explicitly address the research questions in sufficient detail. Our inclusion criteria clearly mentioned that selected papers should answer the research questions. However, once we started reading the papers we noticed that several issues were not clearly presented in the paper.

It is important to note that our goal for this systematic mapping study was providing an overview of current literature in software ecosystems; it was outside the scope of the paper to evaluate the quality of studied papers or explain specific findings. Further analysis is needed to address these specific issues. The mapping study confirms that software ecosystem is an emergent field. The first study was published in 2003 (S43), the last two years were the most productive period. The outcomes of our study indicate that software ecosystem research has been mainly inspired by studies from business and natural ecosystems [ 2,3 ] as well as traditional software engineering research on software product lines, software evolution and open source software. Currently, there are two key communities investigating the topic: digital ecosystem from MEDES conference and IWSECO community. We observed these two communities generally do not cite each other. Even though, both areas have inherited similar concepts from business ecosystems. To strengthen the field, we suggest more collaboration between both communities. Results reveal that key areas investigated by the papers were open source software, ecosystems modeling and business issues. Academic institutions published 70% of papers. This finding reinforces the current trend in software engineering where academics publish the majority of papers. A promising result that highlights the industrial relevance of software ecosystems is that 10 papers presented case studies. However, the methodology rigor and quality of these case studies were not carefully assessed. Our preliminary opinion on this issue is that software ecosystems need more industrial studies to increase the body of evidence in the field. Given the current state of research and practice in software ecosystems, we envisage the need to conduct integrative studies among research communities and industry.

Acknowledgments

We are grateful to authors in the field of software ecosystems for their contribution to conduct this study.

Appendix: The selected studies

ID S1 S2 S3 S4 S5 S6

Reference

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